This dataset contains annual average CES data for California statewide and areas from 1990 - 2023. The Current Employment Statistics (CES) program is a Federal-State cooperative effort in which monthly surveys are conducted to provide estimates of employment, hours, and earnings based on payroll records of business establishments. The CES survey is based on approximately 119,000 businesses and government agencies representing approximately 629,000 individual worksites throughout the United States. CES data reflect the number of nonfarm, payroll jobs. It includes the total number of persons on establishment payrolls, employed full- or part-time, who received pay (whether they worked or not) for any part of the pay period that includes the 12th day of the month. Temporary and intermittent employees are included, as are any employees who are on paid sick leave or on paid holiday. Persons on the payroll of more than one establishment are counted in each establishment. CES data excludes proprietors, self-employed, unpaid family or volunteer workers, farm workers, and household workers. Government employment covers only civilian employees; it excludes uniformed members of the armed services. The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS.

The Business Dynamics Statistics (BDS) includes measures of establishment openings and closings, firm startups, job creation and destruction by firm size, age, and industrial sector, and several other statistics on business dynamics. The U.S. economy is comprised of over 6 million establishments with paid employees. The population of these businesses is constantly churning -- some businesses grow, others decline and yet others close. New businesses are constantly replenishing this pool. The BDS series provide annual statistics on gross job gains and losses for the entire economy and by industrial sector, state, and MSA. These data track changes in employment at the establishment level, and thus provide a picture of the dynamics underlying aggregate net employment growth.

There is a longstanding interest in the contribution of small businesses to job and productivity growth in the U.S. Some recent research suggests that it is business age rather than size that is the critical factor. The BDS permits exploring the respective contributions of both firm age and size.

BDS is based on data going back through 1976. This allows business dynamics to be tracked, measured and analyzed for young firms in their first critical years as well as for more mature firms including those that are in the process of reinventing themselves in an ever changing economic environment.

If you need help understanding the terms used, check out these definitions.

Data Dictionary

Key	List of...	Comment	Example Value
State	String	The state that this report was made for (full name, not the two letter abbreviation).	"Alabama"
Year	Integer	The year that this report was made for.	1978
Data.DHS Denominator	Integer	The Davis-Haltiwanger-Schuh (DHS) denominator is the two-period trailing moving average of employment, intended to prevent transitory shocks from distorting net growth. In other words, this value roughly represents the employment for the area, but is resistant to sudden, spiking growth.	972627
Data.Number of Firms	Integer	The number of firms in this state during this year.	54597
Data.Calculated.Net Job Creation	Integer	The sum of the Job Creation Rate minus the Job Destruction Rate.	74178
Data.Calculated.Net Job Creation Rate	Float	The sum of the Job Creation Rate and the Job Destruction Rate, minus the Net Job Creation Rate.	7.627
Data.Calculated.Reallocation Rate	Float	The sum of the Job Creation Rate and the Job Destruction Rate, minus the absolute Net Job Creation Rate.	29.183
Data.Establishments.Entered	Integer	The number of establishments that entered during this time. Entering occurs when an establishment did not exist in the previous year.	10457
Data.Establishments.Entered Rate	Float	The number of establishments that entered during this time divided by the number of establishments. Entering occurs when an establishment did not exist in the previous year.	16.375
Data.Establishments.Exited	Integer	The number of establishments that exited during this time. Exiting occurs when an establishment has positive employment in the previous year and zero this year.	7749
Data.Establishments.Exited Rate	Float	The number of establishments that exited during this time divided by the number of establishments. Exiting occurs when an establishment has positive employment in the previous year and zero this year.	12.135
Data.Establishments.Physical Locations	Integer	The number of establishments in this region during this time.	65213
Data.Firm Exits.Count	Integer	The number of firms that exited this year.	5248
Data.Firm Exits.Establishment Exit	Integer	The number of establishments exited because of firm deaths.	5329
Data...

Context:

The Occupational Employment Statistics (OES) and National Compensation Survey (NCS) programs have produced estimates by borrowing from the strength and breadth of each survey to provide more details on occupational wages than either program provides individually. Modeled wage estimates provide annual estimates of average hourly wages for occupations by selected job characteristics and within geographical location. The job characteristics include bargaining status (union and nonunion), part- and full-time work status, incentive- and time-based pay, and work levels by occupation.

Direct estimates are based on survey responses only from the particular geographic area to which the estimate refers. In contrast, modeled wage estimates use survey responses from larger areas to fill in information for smaller areas where the sample size is not sufficient to produce direct estimates. Modeled wage estimates require the assumption that the patterns to responses in the larger area hold in the smaller area.

The sample size for the NCS is not large enough to produce direct estimates by area, occupation, and job characteristic for all of the areas for which the OES publishes estimates by area and occupation. The NCS sample consists of 6 private industry panels with approximately 3,300 establishments sampled per panel, and 1,600 sampled state and local government units. The OES full six-panel sample consists of nearly 1.2 million establishments.

The sample establishments are classified in industry categories based on the North American Industry Classification System (NAICS). Within an establishment, specific job categories are selected to represent broader occupational definitions. Jobs are classified according to the Standard Occupational Classification (SOC) system.

Content:

Summary: Average hourly wage estimates for civilian workers in occupations by job characteristic and work levels. These data are available at the national, state, metropolitan, and nonmetropolitan area levels.

Frequency of Observations: Data are available on an annual basis, typically in May.

Data Characteristics: All hourly wages are published to the nearest cent.

Acknowledgements:

This dataset was taken directly from the Bureau of Labor Statistics and converted to CSV format.

Inspiration:

This dataset contains the estimated wages of civilian workers in the United States. Wage changes in certain industries may be indicators for growth or decline. Which industries have had the greatest increases in wages? Combine this dataset with the Bureau of Labor Statistics Consumer Price Index dataset and find out what kinds of jobs you would need to afford your snacks and instant coffee!

The Annual Respondents Database (ARD) is constructed from a compulsory business survey. Until 1997 it was created out of the Annual Censuses of Production and Construction (ACOP and ACOC); these were combined into the Annual Business Inquiry (ABI) in 1998. The ARD is a census of large businesses, and a sample of smaller ones. Smaller firms may receive a "short form". These do not require detailed breakdowns of totals. Hence for certain variables the values may be imputed from third party sources or estimated rather than returned by respondents.

This dataset is created for the Economic Analysis and Satellite Accounts Division for research purposes. To create the ARD, the other surveys are converted into a single consistent format linked by the Inter-Departmental Business Register references over time. Northern Ireland data is held up to 2001. From 2002, the ABI is collected and stored separately in Northern Ireland. Special permission is required to use new NI ABI data.

ABI background
The ABI is the financial information survey conducted by the Office for National Statistics (ONS). This is a statutory survey conducted under the Statistics of Trade Act 1947. Organisations are obliged under this legislation to provide a response. Businesses are sampled from the ONS business register current at the time of drawing the sample: first the CSO Business Register, which ran until 1993; then the Inter-Departmental Business Register, which has run from 1994 onwards. The ONS holds firms' responses to the ABI in the Annual Respondents Database (ARD).

The ABI replaced the following annual survey systems in 1998:

• Annual Employment Survey (AES)
• Annual Censuses of Production and Construction (ACOP/ACOC), which include the Purchases Inquiry (PI)
• The six annual Distribution and Services (DSI) inquiries (Annual Wholesale Inquiry; Annual Retail Inquiry; Annual Motor Trades Inquiry; Annual Catering Inquiry; Annual Property Inquiry; and Annual Service Trades Inquiry

Until 1997 the data were limited to the production and construction industries surveyed by the ACOP and ACOC (construction from 1993 only). The incorporation of the DSI inquiries for six additional sectors is reflected in the number of individual business contributors rising from approximately 15,000 for 1980 to 1996 to approximately 50,000 for 1997/98 and to over 70,000 for 1999.

The ABI is one of the most comprehensive surveys undertaken of business organisations in the UK, covering over 100 key economic variables, and approximately two-thirds of the UK economy. Detailed variables for turnover, employment, costs, capital and the derivation of sales and profits are included. A firm-level measure of Gross Value Added (GVA) is also generated so that the productivity of organisations can be evaluated.

The ABI samples UK businesses and other such establishments according to their employment size and industry sector. It is a census of large businesses, and a stratified sample of small and medium sized enterprises. The stratified sampling framework means that smaller firms move in and out of the survey. The forms are customised for industry sectors and sub-sectors. The statistics produced from the sample data are used primarily to assist in the generation of the National Accounts and the measurement of Gross Domestic Product (GDP).

A number of different form-types are used in the survey. Long form-types are sent to all businesses with an employment of 250 or more and also to a proportion of selected businesses with lower employment. Short form-types are sent to the remaining selected businesses. The forms differ in that long form-types ask for a detailed breakdown of purchases; employment costs; taxes, duties and levies etc, whereas short form-types just ask for the totals of these variables.

The data are collected in two parts: Part 1 is an employment record, collected as soon as possible after 12th December. Part 2 is for financial information, which may be submitted up to twelve months after the financial year end.

Geographical references: postcodes
The postcodes available in these data are pseudo-anonymised postcodes. The real postcodes are not available due to the potential risk of identification of the observations. However, these replacement postcodes retain the inherent nested characteristics of real postcodes, and will allow researchers to aggregate observations to other geographic units, e.g. wards, super output areas, etc. In the dataset, the variable of the replacement postcode is 'new_PC'.

Linking to other business studies
These data contain Inter-Departmental Business Register reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

ARD, the Annual Business Survey (ABS) and the Business Register and Employment Survey (BRES)
The ABI, Part 2 (ABI/2) was replaced by the ABS in 2009. The ABI, Part 1 (ABI/1) was replaced by the BRES in 2009. The BRES data for 2009 onwards are held separately under UK Data Archive SN 7463. ABS data for 2008 onwards are held under UK Data Archive SN 7451. Researchers who are applying for access to the ARD and who require data for 2009 onwards are recommended to also apply for the ABS data under SN 7451.

Key Table Information.Table Title.Island Areas: Comparative Statistics by Manufacturing Industry for Puerto Rico: 2022 and 2017.Table ID.ISLANDAREASIND2022.IA2200IND12.Survey/Program.Economic Census of Island Areas.Year.2022.Dataset.ECNIA Economic Census of Island Areas.Source.U.S. Census Bureau, 2022 Economic Census of Island Areas, Core Statistics.Release Date.2024-12-19.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.2022 Economic Census of Island Areas tables are released on a flow basis from June through December 2024.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe. The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in Puerto Rico, have paid employees, and are classified in one of eighteen in-scope sectors defined by the 2022 NAICS..Sponsor.U.S. Department of Commerce.Methodology.Data Items and Other Identifying Records.Number of establishmentsAnnual payroll ($1,000)Number of employeesNumber of production workers, average for yearProduction workers hoursProduction workers wages ($1,000)Value added ($1,000)Total cost of supplies and/or materials ($1,000)Sales, value of shipments, or revenue ($1,000)Range indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesRange indicating imputed percentage of total sales, value of shipments, or revenueDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the Economic Census of Island Areas are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed..Geography Coverage.The data are shown for employer establishments and firms that vary by industry:At the Territory level for Puerto RicoFor information about economic census geographies, including changes for 2022, see Economic Census: Economic Geographies..Industry Coverage.The data are shown for Puerto Rico at the 2- through 3-digit 2022 NAICS code levels for the manufacturing industry.For information about NAICS, see Economic Census Code Lists..Sampling.The Economic Census of Island Areas is a complete enumeration of establishments located in the islands (i.e., all establishments on the sampling frame are included in the sample). Therefore, the accuracy of tabulations is not affected by sampling error..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY24-0044).The primary method of disclosure avoidance protection is noise infusion. Under this method, the quantitative data values such as sales or payroll for each establishment are perturbed prior to tabulation by applying a random noise multiplier (i.e., factor). Each establishment is assigned a single noise factor, which is applied to all its quantitative data value. Using this method, most published cell totals are perturbed by at most a few percentage points.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. For more information on disclosure avoidance, see Methodology for the 2022 Economic Census- Island Areas..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, see Methodology for the 2022 Economic Census- Island Areas.For more information about survey questionnaires, Primary Business Activity/NAICS codes, and NAPCS codes, see Economic Census Technical Documentation..Weights.Because the Economic Census of Island Areas is a complete enumeration, there is no sample weighting..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector00.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.X - Not applicableA - Relative standard error of 100% or morer - Reviseds - Relative standard error exceeds 40%For a complete list of symbols, see Economic Census Data Dictionary..Data-Specific Notes.Data users who crea...

Analysis of ‘Average wages of the main job by period, type of working day, Number of persons working in the establishment and decile. EPA (API identifier: 13943)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/urn-ine-es-tabla-t3-348-13943 on 07 January 2022.

--- Dataset description provided by original source is as follows ---

Table of INEBase Average wages of the main job by period, type of working day, Number of persons working in the establishment and decile. Annual. National. Economically Active Population Survey

--- Original source retains full ownership of the source dataset ---

Flights without a full package, Annual Average, Use electronic programs, Government Procedures and Bureaucracy, Workers, Total tourism establishments, Constraints facing setting up or practicing economic activities, Electricity Price, Access to Telecommunication (Phone & Internet), Road Passenger Transport, Number of international passengers, Water Price, Percentage distribution of tourism establishments which use electronic programs, Flights within a full package, Low demand, Percentage %, Labour Laws & Regulations, Electricity Supply (without interruption), Laptop, Percentage distribution of tourism establishments which use social media, Employees, Hotel rooms, Salaries and wages, Availability of Skilled Labour, Inbound international flights, Tourism Direct Gross Value Added, Water Passenger Transport, Number of local passengers, Average duration of residence in accommodation units, Other Activities, Professionals, Other Activities, Non-cloud Data, Railways Passenger Transport, Percentage distribution of devices used in tourism establishments, Operating surplus, Fuel Price, Managers, nights, Operating expenditure, Main Activity, Non-Saudi, Operating rate of international flights, Major performance indicators for passengers transport services, Licenses & Permits, Do not use social media, Cultural Activities, Outbound international flights, Land Passenger Transport, Workers problems, Furniture Apartments, Major challenges facing business environment development, There are constraints, Female, Transport Equipment Rental, Percentage distribution of tourism establishments that have cloud data, Number of available seats for international flights, Average daily price for accommodation units in Saudi Riyal, Number of available seats for local flights, Operating revenues distribution, Food and Beverage Serving Activities, Local Competition, Travel Agencies and Reservation Services, Cloud Data, Operating rate of local flights, Do not have Accounting Books, Security & Stability, Benefits and allowances, Water Supply (without interruption), Railway Passenger Transport, Percentage distribution of accounting books or budget usage, Percentage of sold flights for passengers by flight type, Operating revenues, Other Specific Tourism Characteristic Services, Government Inspection Procedures, Number, Fuel Supply (without interruption), Access to Finance, Thousands Riyals, Accommodation for Visitors, Total compensations, Gross value added of the tourism industries = operating revenues - operating expenses, Technicians, Local flights, Saudi Riyal per day, Total, Do not use electronic programs, Land / Rent of Space, Have Accounting Books, Retail trade of Country-Specific Tourism Characteristic Goods, No constraints, Air Passenger Transport, Employment percentage, Saudi, Occupancy rate for accommodation units, Handheld or tablet, Average daily income for accommodation units in Saudi Riyal, Desktop (PC), Specialists, -, Sports and Recreational Activities, Use social media, Number of employees, wages and Salaries, compensation, Flight, Tourism Establishments Survey, Economic Activity, Occupations

Saudi Arabia

Explore the Tourism Establishments Survey dataset in Saudi Arabia to uncover key insights on economic activities, workers, government procedures, and more. Access data on airline passengers, accommodation rates, transportation services, and challenges facing the tourism industry. Discover valuable information to enhance your understanding of the tourism sector in Saudi Arabia.Follow data.kapsarc.org for timely data to advance energy economics research..Preliminary estimated data based on supply and use tables.Gross value added of the tourism industries = operating revenues - operating expenses.Notes:Full package deals: packages that include the flight ticket as well as other services, such as the hotel booking, car rental... etc. Source: Administrative data from the Ministry of Human Resources and Social Development, Ministry of Tourism and the Saudi Railway Company

The Occupational Employment and Wage Statistics (OES) program conducts a semi-annual survey to produce estimates of employment and wages for specific occupations. The OES program collects data on wage and salary workers in nonfarm establishments in order to produce employment and wage estimates for about 800 occupations. Data from self-employed persons are not collected and are not included in the estimates. The OES program produces these occupational estimates by geographic area and by industry. Estimates based on geographic areas are available at the National, State, Metropolitan, and Nonmetropolitan Area levels. The Bureau of Labor Statistics produces occupational employment and wage estimates for over 450 industry classifications at the national level. The industry classifications correspond to the sector, 3-, 4-, and 5-digit North American Industry Classification System (NAICS) industrial groups. More information and details about the data provided can be found at http://www.bls.gov/oes

This dataset presents a combined view of economic indicators for the hotels and restaurants sector in Qatar. It aggregates data from establishments of all sizes and includes metrics such as operating surplus, employee compensation, value added per worker, productivity, average annual wage, and input-output ratios.

Explore wages and salaries data by establishment size and economic activity in Saudi Arabia. This dataset covers various industries such as manufacturing, health, financial intermediation, education, construction, and more.

Other manufacturing, Remediation activities and other waste management services, Industry of paper and its products, Health and social work, Extraction of crude petroleum and natural gas, Social work activities without accommodation, Manufacture of food prod. and beverages, Manufacture of textiles, Financial intermediation, Motion picture, video & tv programme production, sound recording, Scientific research and development, Hotels and restaurants, Other personal service activities, Retail trade, except of motor vehicles and motorcycles, Information service activities, Manufacturing of apparel, preparing & tanning fur, Food and beverage service activities, Manufacture of food products, Manufacture of leather and related products, Repair and installation of machinery and equipment, Programming and broadcasting activities, Other mining and quarrying, Education, Manufacture of office, accounting and computing machinery, Creative, arts and entertainment activities, Insurance and pension funding, except compulsory social security, Construction, Sports activities and amusement and recreation activities, Printing and reproduction of recorded media, Travel agency, tour operator, reservation service & related activities, Computer programming, consultancy and related activities, Repair of computers and personal and household goods, Agriculture and hunting and related service activities, Manufacture of furniture, Activities auxiliary to financial intermediation, Fishing and aquaculture, Mining of coal and lignite, Manufacture of electrical machinery and apparatus, Advertising and market research, Printing & Publishing, Manufacture of radio, television and communication equipment and apparatus, Activities of head offices; management consultancy activities, Activities for mining and quarrying, Rental and leasing activities, Services to buildings and landscape activities, Office administrative, office support & other business support act's, Forestry and logging, Manufacture of other non-metallic mineral products, Air transport, Manufacture of furniture; manufacturing, Mining support service activities, Accommodation, Crop and animal production, hunting and related service activities, Post and telecommunications, Water collection, treatment and supply, Manufacture of machinery and equipment n.e.c., Land transport and transport via pipelines, Manufacture of medical, precision and optical instruments, watches and clocks, Manufacture of beverages, Activities of membership organizations n.e.c., Manufacture of non-metallic mineral products, Water transport, Wholesale trade, except of motor vehicles and motorcycles, Manufacture of products and preparations pharmaceutical, Wholesale & retail trade and repair of motor vehicles & motorcycles, Land transport; transport via pipelines, Manufacture of wood and of products of wood and cork, Real estate activities, Activities of membership organizations, Warehousing and support activities for transportation, Manufacture of wearing apparel, Legal and accounting activities, Manufacture of electrical equipment, Financial service activities, except insurance and pension funding, Architectural and engineering activities; technical testing & analysis, Manufacture of fabricated metal products, Manufacture of coke and refined petroleum products, Tanning and dressing of leather; manufacture of luggage and footwear, Retail trade and repair of personal and household goods, Supporting and auxiliary transport activities; activities of travel agencies, Sewerage, Activities, business services, Exploration of oil and natural gas, Publishing activities, Specialized construction activities, Insurance, reinsurance and pension funding, Employment activities, Manufacture of motor vehicles, trailers and semi-trailers, Construction of buildings, Libraries, archives, museums and other cultural activities, Mining of metal ores, Electricity, gas, steam and air conditioning supply, Wholesale trade and commission trade, service activities, Recycling, Manufacture of basic metals, Activities auxiliary to financial service and insurance activities, Recreational, cultural and sporting activities, Waste collection, treatment & disposal activities; materials recovery, Manufacture of computer, electronic and optical products, Veterinary activities, Fishing, Manufacture of tobacco products, Manufacture of machinery and equipment, Manufacture of paper and paper products, Security and investigation activities, Postal and courier activities, Residential care activities, Civil engineering, Computer and related activities, Human health activities, Total, Products of refined petroleum, Manufacture of chemicals , Articles and products, Sale, maintenance and repair of motor vehicles and motorcycles; retail sale of automotive fuel, Renting of machinery and equipment without operator and of personal and household goods, Manufacture of chemicals and chemical products, Telecommunications, Manufacture of other transport equipment, Collection, purification and distribution of water, Sewage and refuse disposal and sanitation, Electricity, gas and steam, Other professional, scientific and technical activities, Manufacture of rubber and plastics products, Research and development, Labor, Annual Economic Establishment Survey, Manufacturing

Saudi ArabiaFollow data.kapsarc.org for timely data to advance energy economics research..Data from the Annual Economic Establishment Survey.Do not include establishments operating in the governmental and external sectors. Including establishments operating in the private and public sector and not for profit.

This layer contains data on the number of establishments, total employment, and total annual payroll for for 20 selected 4- and 5-digit North American Industry Classification System (NAICS) codes. This is shown by county and state boundaries. The full CBP data set (available at census.gov) is updated annually to contain the most currently released CBP data. This layer is symbolized to show the total number of establishments depicted by size, and the average annual pay per employee, depicted by color.

Current Vintage: 2017

CBP Table: CB1700CBP

Data downloaded from: Census Bureau's API for County Business Patterns

Date of API call: June 1, 2019

The United States Census Bureau's County Business Patterns Program (CBP):

About this Program Data Technical Documentation News & Updates

This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census Bureau and CBP when using this data.

Data Processing Notes: Boundaries come from the US Census Bureau TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census Bureau. These are Census Bureau boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 56 records - all US states, Washington D.C., Puerto Rico, and U.S. Island Areas Blank values represent industries where there either were no businesses in that industry and that geography OR industries where the data had to be withheld to avoid disclosing data for individual companies. Users should visit data.census.gov or Census Business Builder for more details on these withheld records.

Release Date: 2018-08-10.[NOTE: Includes firms with payroll at any time during 2016. Employment reflects the number of paid employees during the March 12 pay period. Data are based on Census administrative records, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2016 Annual Survey of Entrepreneurs. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Respondent firms include all firms that responded to the characteristic(s) tabulated in this dataset and reported gender, ethnicity, race, or veteran status for at least one owner and were not publicly held or not classifiable by gender, ethnicity, race, and veteran status. The 2016 Annual Survey of Entrepreneurs asked for information for up to four persons owning the largest percentage(s) of the business. Percentages are for owners of respondent firms only and are not recalculated when the dataset is resorted. Percentages are always based on total reporting (defined above) within a gender, ethnicity, race, veteran status, and/or industry group for the characteristics tabulated in this dataset. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for Owners of Respondent Employer Firms by Owner's Average Number of Hours Per Week Spent Managing or Working in the Business by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2016. ..Release Schedule. . This file was released in August 2018.. ..Key Table Information. . These data are related to all other 2016 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2016 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2016 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. For Characteristics of Business Owners (CBO) data, all estimates are of owners of firms responding to the ASE. That is, estimates are based only on firms providing gender, ethnicity, race, or veteran status; or firms not classifiable by gender, ethnicity, race, and veteran status that returned an ASE online questionnaire with at least one question answered. The ASE online questionnaire provided space for up to four owners to report their characteristics.. CBO data are not representative of all owners of all firms operating in the United States. The data do not represent all business owners in the United States.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for Owners of Respondent Employer Firms by Owner's Average Number of Hours Per Week Spent Managing or Working in the Business by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2016 contains data on:. . Number of owners of respondent firms with paid employees. Percent of number of owners of respondent firms with paid employees. . The data are shown for:. . Gender, ethnicity, race and veteran status of owners of respondent firms. . All owners of respondent firms. Female. Male. Hispanic. Non-Hispanic. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Some other race. Minority. Nonminority. Veteran. Nonveteran. . . Years in business. . All firms. Firms less than 2 years in business. Firms with 2 to 3 years in business. Firms with 4 to 5 years in business. Firms with 6 to 10 years in business. Firms with 11 to 15 years in business. Firms with 16 or more years in business. . . Owner...

Reference Layer: County Business Patterns (CBP) from Economic Census 2017This layer contains data on the number of establishments, total employment, and total annual payroll for for 20 selected 4- and 5-digit North American Industry Classification System (NAICS) codes. This is shown by county and state boundaries. The full CBP data set (available at census.gov) is updated annually to contain the most currently released CBP data. This layer is symbolized to show the total number of establishments depicted by size, and the average annual pay per employee, depicted by color. Current Vintage: 2017CBP Table: CB1700CBPData downloaded from: Census Bureau's API for County Business Patterns Date of API call: June 1, 2019 The United States Census Bureau's County Business Patterns Program (CBP):About this ProgramDataTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census Bureau and CBP when using this data. Data Processing Notes:Boundaries come from the US Census Bureau TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census Bureau. These are Census Bureau boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 56 records - all US states, Washington D.C., Puerto Rico, and U.S. Island AreasBlank values represent industries where there either were no businesses in that industry and that geography OR industries where the data had to be withheld to avoid disclosing data for individual companies. Users should visit data.census.gov or Census Business Builder for more details on these withheld records

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Bolivia in 2006, 2010 and 2017, as part of Latin America and the Caribbean Enterprise Surveys rollout, an initiative of the World Bank. The objective of the Enterprise Survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms.

Enterprise Surveys target a sample consisting of longitudinal (panel) observations and new cross-sectional data. Panel firms are prioritized in the sample selection, comprising up to 50% of the sample. For all panel firms, regardless of the sample, current eligibility or operating status is determined and included in panel datasets.

Bolivia ES 2010 was conducted in June 2010 and October 2010, Bolivia ES 2016 was carried out in January and June of 2017. Stratified random sampling was used to select the surveyed businesses. Data was collected using face-to-face interviews.

Data from 1,339 establishments was analyzed: 433 businesses were from 2006 only, 97 - from 2010 only, 197 - from 2017 only, 170 firms were from 2010 and 2017, 196 - from 2006 and 2010, 246 firms were from 2006, 2010 and 2017.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively measure characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed as follows: the universe was stratified into Manufacturing industries (ISIC Rev. 3.1 codes 15- 37), Retail industries (ISIC code 52) and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72).

Size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

The sample frame for 2017 consisted of listings of firms from two sources: for panel firms the list of 362 firms from the Bolivia 2010 ES was used, and for fresh firms (i.e., firms not covered in 2010) Economic Census, updated by Encuestas y Estudios (2016) was used.

In 2010, regional stratification was defined in three locations (city and the surrounding business area): La Paz, Santa Cruz, and Cochabamba.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

This table presents a number of key figures of the sector accounts. These main indicators provide the most important information on the total economy and on the main institutional sectors of the economy: non-financial corporations, financial corporations, general government, households including non-profit institutions serving households and the rest of the world.

Data available from: Annual figures from 1995. Quarterly figures from first quarter 1999.

Status of the figures: Annual figures from 1995 up to and including 2023 are final. Quarterly data from 2023 are provisional.

Changes as of June 24th, 2025: Data on the first quarter of 2025 have been added. Following revision policy, 2023 and 2024 data are updated, and time series of the sector accounts are revised (annual revision).

Adjustment as of April 14th 2025: Quarterly and annualy data of general government debt (EMU) of 2024 were incorrectly hidden in the last version of this table. This has been adjusted in this version.

Adjustment as of April 10th 2025: Due to an error made while processing the data, the initial preliminary figures for government expenditure in 2024 were calculated incorrectly, which means that the figure published for the general government balance was also incorrect. We refer to the Government Finance Statistics for the current figures. Links to the Government Finance Statistics could be found in paragraph 3. Until the publication end of June the Sector accounts therefore diverge from the Government Finance Statistics.

When will new figures be published? Annual figures: The first annual data are published 85 day after the end of the reporting year as the sum of the four quarters of the year. Subsequently provisional data are published 6 months after the end of the reporting year. Final data are released 18 months after the end of the reporting year. Furthermore the sector accounts are annually revised for all reporting periods. These data are published each year in June. Quarterly figures: The first quarterly estimate is available 85 days after the end of each reporting quarter. The first quarter may be revised in September, the second quarter in December. Should further quarterly information become available thereafter, the estimates for the first three quarters may be revised in March. If (new) annual figures become available in June, the quarterly figures will be revised again to bring them in line with the annual figures. Please note that there is a possibility that adjustments might take place at the end of March or September, in order to provide the European Commission with the latest annual and quarterly figures.

This is the publicly-accessible portion of the dataset used to conduct the analysis for this study. It contains the following variables: a scrambled establishment ID that uniquely identifies each establishment, the establishment’s city, industry, year, year of random inspection, treated (has been randomly inspected), sales, employment, PAYDEX score, Composite Credit Appraisal. This dataset does not contain the following variables used in the analysis because of the confidentiality conditions under which they were obtained: establishment name, street address, ZIP code, DUNS number; annual payroll, injury count, injury cost, and average occupational riskiness. Researchers seeking full access to data on establishment names, addresses, DUNS numbers, sales, employment, PAYDEX scores, Composite Credit Appraisals, and industry (NAICS and SIC Codes) from the National Establishment Time-Series (NETS) database can contact Donald Walls, President, Walls & Associates (tel. +1-510-763-0641, dwalls2@earthlink.net). Researchers interested in obtaining data on the number and costs of workers’ compensation claims, occupational riskiness, payroll, and establishment names and addresses from the Workers’ Compensation Insurance Rating Bureau of California (WCIRB) may contact WCIRB’s Chief Actuary Dave Bellusci (tel. +1-415-777-0777, dbellusci@wcirbonline.org).

Each year, County departments and agencies report performance data on core activities for public viewing on the County’s website. This dataset contains these reports for all past years starting in 2018. recordKey: A unique identifier consisting of, respectively, a code for the department and the numbers of the goal, objective, and measureGoal: Encompasses one or more objectivesObjective: A subdivision of a goal, encompasses one or more measuresTimeframe: Either Calendar Year or Fiscal Year. For example, the 2023 fiscal year began on July 1, 2022, and ended on June 30, 2023.Measure: The specific result being measuredMeasure Type: Resource (Input); Workload, Demand, Production (Output); Efficiency; Quality; or Impact (Outcome)Units: Number; Percentage; Average; or DollarsYear (for example ‘2018): The amount reported by the department for the listed measure in this fiscal or calendar year

Release Date: 2017-07-13.[NOTE: Includes firms with payroll at any time during 2015. Employment reflects the number of paid employees during the March 12 pay period. Data are based on Census administrative records, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2015 Annual Survey of Entrepreneurs. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Respondent firms include all firms that responded to the characteristic(s) tabulated in this dataset and reported gender, ethnicity, race, or veteran status for at least one owner and were not publicly held or not classifiable by gender, ethnicity, race, and veteran status. The 2015 Annual Survey of Entrepreneurs asked for information for up to four persons owning the largest percentage(s) of the business. Percentages are for owners of respondent firms only and are not recalculated when the dataset is resorted. Percentages are always based on total reporting (defined above) within a gender, ethnicity, race, veteran status, and/or industry group for the characteristics tabulated in this dataset. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for Owners of Respondent Employer Firms by Owner's Average Number of Hours Per Week Spent Managing or Working in the Business by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2015. ..Release Schedule. . This file was released in July 2017.. ..Key Table Information. . These data are related to all other 2015 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2015 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2015 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. For Characteristics of Business Owners (CBO) data, all estimates are of owners of firms responding to the ASE. That is, estimates are based only on firms providing gender, ethnicity, race, or veteran status; or firms not classifiable by gender, ethnicity, race, and veteran status that returned an ASE online questionnaire with at least one question answered. The ASE online questionnaire provided space for up to four owners to report their characteristics.. CBO data are not representative of all owners of all firms operating in the United States. The data do not represent all business owners in the United States.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for Owners of Respondent Employer Firms by Owner's Average Number of Hours Per Week Spent Managing or Working in the Business by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2015 contains data on:. . Number of owners of respondent firms with paid employees. Percent of number of owners of respondent firms with paid employees. . The data are shown for:. . Gender, ethnicity, race and veteran status of owners of respondent firms. . All owners of respondent firms. Female. Male. Hispanic. Non-Hispanic. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Some other race. Minority. Nonminority. Veteran. Nonveteran. . . Years in business. . All firms. Firms less than 2 years in business. Firms with 2 to 3 years in business. Firms with 4 to 5 years in business. Firms with 6 to 10 years in business. Firms with 11 to 15 years in business. Firms with 16 or more years in business. . . Owner's...

US Automatic Traffic Recorder Stations Data

Vehicle Traffic Counts and Locations at US ATR Stations

By Homeland Infrastructure Foundation [source]

About this dataset

This comprehensive dataset records important information about Automatic Traffic Recorder (ATR) Stations located across the United States. ATR stations play a crucial role in traffic management and planning by continuously monitoring and counting the number of vehicles passing through each station.

The data contained in this dataset has been meticulously gathered from station description files supplied by the Federal Highway Administration (FHWA) for both Weigh-in-Motion (WIM) devices and Automatic Traffic Recorders. In addition to this, location referencing data was sourced from the National Highway Planning Network version 4.0 as well as individual State offices of Transportation.

The database includes essential attributes such as a unique identifier for each ATR station, indicated by 'STTNKEY'. It also indicates if a site is part of the National Highway System, denoted under 'NHS'. Other key aspects recorded include specific locations generally named after streets or highways under 'LOCATION', along with relevant comments providing additional context in 'COMMENT'.

Perhaps one of the most critical factors noted in this data set would be traffic volume at each location, measured by Annual Average Daily Traffic ('AADT'). This metric represents total vehicle flow on roads or highways for a year divided over 365 days — an essential numeric analyst's often call upon when making traffic-related predictions or decisions.

Location coordinates incorporating longitude and latitude measurements of every ATR station are documented clearly — aiding geospatial analysis. Furthermore, X and Y coordinates correspond to these locations facilitating accurate map plotting.

Additional information contained also includes postal codes labeled as 'STPOSTAL' where stations are located with respective state FIPS codes indicated under ‘STFIPS’. County specific FIPS code are documented within ‘CTFIPS’. Versioning information helps users track versions ensuring they work off latest datasets with temporal geographic attribute updates captured via ‘YEAR_GEO’.

Reference Source: Click Here

How to use the dataset

Introduction

Diving into the data

The dataset comprises a collection of attributes for each station such as its location details (latitude, longitude), AADT or The Annual Average Daily Traffic amount, classification of road where it's located etc. Additionally, there is information related to when was this geographical information last updated.

Understanding Columns

Here's what primary columns represent: - Sttnkey: A unique identifier for each station. - NHS: Indicates if the station is part of national highway system. - Location: Describes specific location of a station with street or highway name. - Comment: Any additional remarks related to that station. - Longitude,Latitude: Geographic coordinates. - STPostal: The postal code where a given station resides. - menu 4 dots indicates show more items** - ADT: Annual Average Daily Traffic count indicating average volume of vehicles passing through that route annually divided by 365 days - Year_GEO: The year when geographic information was last updated - can provide insight into recency or timeliness of recorded attribute values - Fclass: Road classification i.e interstate,dis,e tc., providing context about type/stature/importance or natureof theroad on whichstationlies 11.Stfips,Ctfips- FIPS codes representing state,county respectively

Using this information

Given its structure and contents,thisdatasetisveryusefulforanumberofpurposes:

1.Urban Planning & InfrastructureDevelopment Understanding traffic flows and volumes can be instrumental in deciding where to build new infrastructure or improve existing ones. Planners can identify high traffic areas needing more robust facilities.

2.Traffic Management & Policies Analysing chronological changes and patterns of traffic volume, local transportation departments can plan out strategic time-based policies for congestion management.

3.Residential/CommercialRealEstateDevelopment Real estate developers can use this data to assess the appeal of a location based on its accessibility i.e whether it sits on high-frequency route or is located in more peaceful, low-traffic areas etc

4.Environmental AnalysisResearch: Re...

This layer contains data on the number of establishments, total employment, and total annual payroll for for 20 selected 4- and 5-digit North American Industry Classification System (NAICS) codes. This is shown by county and state boundaries. The full CBP data set (available at census.gov) is updated annually to contain the most currently released CBP data. This layer is symbolized to show the total number of establishments depicted by size, and the average annual pay per employee, depicted by color. Current Vintage: 2022CBP Table: CB1700CBPData downloaded from: Census Bureau's API for County Business Patterns Date of API call: January 2, 2025 The United States Census Bureau's County Business Patterns Program (CBP):About this ProgramDataTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census Bureau and CBP when using this data. Data Processing Notes:Boundaries come from the US Census Bureau TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census Bureau. These are Census Bureau boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 56 records - all US states, Washington D.C., Puerto Rico, and U.S. Island AreasBlank values represent industries where there either were no businesses in that industry and that geography OR industries where the data had to be withheld to avoid disclosing data for individual companies. Users should visit data.census.gov or Census Business Builder for more details on these withheld records.